abidlabs / mnist-cnn-test

huggingface.co
Total runs: 10
24-hour runs: 0
7-day runs: -21
30-day runs: -12
Model's Last Updated: September 11 2026
image-classification

Introduction of mnist-cnn-test

Model Details of mnist-cnn-test

MNIST CNN (1-minute test run)

A small CNN trained from scratch on ylecun/mnist for a fixed 60-second wall-clock budget (13129 steps, ~28.01 epochs).

Results
Metric Value
Test accuracy 0.9939
Test loss 0.0232
Training wall-clock 60.0s
Steps 13129
Throughput 27993 images/s
Architecture

Conv(1->32, 3x3) -> ReLU -> MaxPool(2) -> Conv(32->64, 3x3) -> ReLU -> MaxPool(2) -> Flatten -> Linear(3136->128) -> ReLU -> Linear(128->10) . Defined in model.py .

Usage
import torch
from model import SmallCNN

model = SmallCNN()
state = torch.load("model.pt", map_location="cpu")
model.load_state_dict(state["model_state_dict"])
model.eval()

# Input: float tensor (N, 1, 28, 28) scaled to [0, 1], then normalized
x = (x - 0.1307) / 0.3081
logits = model(x)
pred = logits.argmax(1)

Training preprocessing also applied random translation (pad 2, random crop) to the train split.

Runs of abidlabs mnist-cnn-test on huggingface.co

10
Total runs
0
24-hour runs
-3
3-day runs
-21
7-day runs
-12
30-day runs

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https://huggingface.co/abidlabs/mnist-cnn-test

mnist-cnn-test install

mnist-cnn-test is an open source model from GitHub that offers a free installation service, and any user can find mnist-cnn-test on GitHub to install. At the same time, huggingface.co provides the effect of mnist-cnn-test install, users can directly use mnist-cnn-test installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

mnist-cnn-test install url in huggingface.co:

https://huggingface.co/abidlabs/mnist-cnn-test

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